HARNESSING LARGE LANGUAGE MODELS FOR HIGH-PERFORMANCE COMPUTING: OPPORTUNITIES AND CHALLENGES

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Volume 7, Article e2025.02, 2025, Pages 1-7

Elviz Ismayilov

Department of General and Applied Mathematics of Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

High-Performance Computing (HPC) is a cornerstone of scientific and engineering advancements, enabling complex computations in areas such as climate modeling, genomics, and artificial intelligence. Concurrently, Large Language Models (LLMs) have emerged as powerful AI-driven tools capable of code optimization, automation, and scientific reasoning. The integration of LLMs into HPC systems presents significant opportunities, including enhanced code generation, improved workload management, and efficient parallel execution. However, this convergence also introduces several challenges, such as high computational costs, scalability issues, memory constraints, security risks, and interpretability concerns. This paper explores the role of LLMs in HPC, discusses existing research and industrial applications, and highlights key challenges and potential solutions. Furthermore, it provides insights into recent advances in AI-powered HPC solutions and presents case studies showcasing real-world implementations. The paper concludes with future research directions, focusing on efficient LLM architectures, integration with emerging HPC technologies, and ethical considerations. The findings emphasize the need for continued innovation to make LLMs more efficient, scalable, and reliable for HPC applications.

Keywords:

High-Performance Computing, Large Language Models, AI-Driven Optimization, Parallel Computing, Scientific Computing, Machine Learning, Code Optimization, Federated Learning, AI Ethics

DOI: https://doi.org/10.32010/26166127.2025.03

Reference 

Amodei, D., Hernandez, D., Sastry, G., Clark, J., Brockman, G., & Sutskever, I. (2018). AI and compute, 2018. URL https://openai. com/blog/ai-and-compute, 4.

Brown, T., Mann, B., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

Jouppi, N. P., Young, C., et al. (2017, June). In-datacenter performance analysis of a tensor processing unit. In Proceedings of the 44th annual international symposium on computer architecture (pp. 1-12).

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

NVIDIA Corporation (2022). AI and HPC: Accelerating Scientific Discovery. Retrieved from https://developer.nvidia.com/hpc-ai

Radford, A., Wu, J., et al. (2019). Language Models: GPT-2. OpenAI.

Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019). Megatron-lm: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053.

Jouppi, N. P., Young, C., et al. (2021). Advanced AI-Driven HPC Scheduling. IEEE Transactions on Computers.

OpenAI. (2023). Codex: An AI System for Code Generation. Retrieved from https://openai.com/codex

Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171-4186).

Vavekanand, R., & Sam, K. (2024). Llama 3.1: An in-depth analysis of the next-generation large language model. Preprint, July.

Ismayilov, E. (2018). Study of Azerbaijani Hand-Printed Characters Recognition System by New Feature Class and Svm Method. Problems of information technology, 9(2), 89-94.